Evaluation of super resolution technology for digestive endoscopic images.

OBJECT

This study aims to evaluate the value of super resolution (SR) technology in augmenting the quality of digestive endoscopic images.

METHODS

In the retrospective study, we employed two advanced SR models, , SwimIR and ESRGAN. Two discrete datasets were utilized, with training conducted using the dataset of the First Affiliated Hospital of Soochow University (12,212 high-resolution images) and evaluation conducted using the HyperKvasir dataset (2,566 low-resolution images). Furthermore, an assessment of the impact of enhanced low-resolution images was conducted using a 5-point Likert scale from the perspectives of endoscopists. Finally, two endoscopic image classification tasks were employed to evaluate the effect of SR technology on computer vision (CV).

RESULTS

SwinIR demonstrated superior performance, which achieved a PSNR of 32.60, an SSIM of 0.90, and a VIF of 0.47 in test set. 90 % of endoscopists supported that SR preprocessing moderately ameliorated the readability of endoscopic images. For CV, enhanced images bolstered the performance of convolutional neural networks, whether in the classification task of Barrett's esophagus (improved F1-score: 0.04) or Mayo Endoscopy Score (improved F1-score: 0.04).

CONCLUSIONS

SR technology demonstrates the capacity to produce high-resolution endoscopic images. The approach enhanced clinical readability and CV models' performance of low-resolution endoscopic images.